Lily AI
The semantic glue between product attributes and consumer search intent for enterprise retail.
AI-driven personalization for fashion retailers to boost conversion and reduce returns.
Intelistyle is a leading AI-driven fashion personalization platform designed for enterprise retailers and e-commerce brands. Its technical architecture leverages sophisticated Computer Vision (CV) and Deep Learning models to analyze garment images, extracting over 1,000 fashion attributes automatically. By 2026, the platform has solidified its position in the market by integrating Generative AI (GANs and Diffusion models) for high-fidelity virtual try-on experiences that adapt to diverse body types. The system's recommendation engine is built on a multi-objective optimization framework, balancing cross-selling goals with individual user style preferences. Intelistyle's 'Complete the Look' feature utilizes a proprietary styling logic that mimics human stylists, trained on millions of fashion datasets. For retailers, it serves as a full-stack omnichannel solution, bridging the gap between digital discovery and physical in-store experiences via smart mirrors and personalized style quizzes. The platform is highly scalable, supporting massive SKU catalogs and real-time processing of high-traffic fashion storefronts like H&M and Lane Crawford.
Uses Generative Adversarial Networks (GANs) to realistically drape garments over user-provided or model photos.
The semantic glue between product attributes and consumer search intent for enterprise retail.
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Computer vision models extract 1000+ attributes (sleeve length, neckline, occasion) from a single image.
Recommendation logic that bundles products into outfits based on visual similarity and trend data.
Vector-based embedding search that allows users to upload a photo to find similar items in stock.
Hardware-agnostic software for physical mirrors that offers digital styling advice in dressing rooms.
An interactive logic tree that uses NLP to categorize user style DNA.
Generates personalized outfit emails and push notifications for users based on browse history.
Customers ordering multiple sizes or styles because they aren't sure how it looks or fits.
Registry Updated:2/7/2026
Customers only buying a single item (e.g., a shirt) without knowing what to pair it with.
Manual product tagging for thousands of new seasonal SKUs is slow and error-prone.